Computer Sciences Commons^™

Forks Over Knives: Predictive Inconsistency In Criminal Justice Algorithmic Risk Assessment Tools, Travis Greene, Galit Shmueli, Jan Fell, Ching-Fu Lin, Han-Wei Liu

Research Collection Yong Pung How School Of Law

Big data and algorithmic risk prediction tools promise to improve criminal justice systems by reducing human biases and inconsistencies in decision-making. Yet different, equally justifiable choices when developing, testing and deploying these socio-technical tools can lead to disparate predicted risk scores for the same individual. Synthesising diverse perspectives from machine learning, statistics, sociology, criminology, law, philosophy and economics, we conceptualise this phenomenon as predictive inconsistency. We describe sources of predictive inconsistency at different stages of algorithmic risk assessment tool development and deployment and consider how future technological developments may amplify predictive inconsistency. We argue, however, that in a diverse and …

Prompting For Multimodal Hateful Meme Classification, Rui Cao, Roy Ka-Wei Lee, Wen-Haw Chong, Jing Jiang

Research Collection School Of Computing and Information Systems

Hateful meme classification is a challenging multimodal task that requires complex reasoning and contextual background knowledge. Ideally, we could leverage an explicit external knowledge base to supplement contextual and cultural information in hateful memes. However, there is no known explicit external knowledge base that could provide such hate speech contextual information. To address this gap, we propose PromptHate, a simple yet effective prompt-based model that prompts pre-trained language models (PLMs) for hateful meme classification. Specifically, we construct simple prompts and provide a few in-context examples to exploit the implicit knowledge in the pretrained RoBERTa language model for hateful meme classification. …

Dialogconv: A Lightweight Fully Convolutional Network For Multi-View Response Selection, Yongkang Liu, Shi Feng, Wei Gao, Daling Wang, Yifei Zhang

Research Collection School Of Computing and Information Systems

Current end-to-end retrieval-based dialogue systems are mainly based on Recurrent Neural Networks or Transformers with attention mechanisms. Although promising results have been achieved, these models often suffer from slow inference or huge number of parameters. In this paper, we propose a novel lightweight fully convolutional architecture, called DialogConv, for response selection. DialogConv is exclusively built on top of convolution to extract matching features of context and response. Dialogues are modeled in 3D views, where DialogConv performs convolution operations on embedding view, word view and utterance view to capture richer semantic information from multiple contextual views. On the four benchmark datasets, …

A Recommendation On How To Teach K-Means In Introductory Analytics Courses, Manoj Thulasidas

Research Collection School Of Computing and Information Systems

We teach K-Means clustering in introductory data analytics courses because it is one of the simplest and most widely used unsupervised machine learning algorithms. However, one drawback of this algorithm is that it does not offer a clear method to determine the appropriate number of clusters; it does not have a built-in mechanism for K selection. What is usually taught as the solution for the K Selection problem is the so-called elbow method, where we look at the incremental changes in some quality metric (usually, the sum of squared errors, SSE), trying to find a sudden change. In addition to …

Bank Error In Whose Favor? A Case Study Of Decentralized Finance Misgovernance, Ping Fan Ke, Ka Chung Boris Ng

Research Collection School Of Computing and Information Systems

Decentralized Finance (DeFi) emerged rapidly in recent years and provided open and transparent financial services to the public. Due to its popularity, it is not uncommon to see cybersecurity incidents in the DeFi landscape, yet the impact of such incidents is under-studied. In this paper, we examine two incidents in DeFi protocol that are mainly caused by misgovernance and mistake in the smart contract. By using the synthetic control method, we found that the incident in Alchemix did not have a significant effect on the total value locked (TVL) in the protocol, whereas the incident in Compound caused a 6.13% …

Non-Negative Matrix Factorization In The Identification Of Co-Mutations, Michael Robert Kolar

Theses and Dissertations

One of the difficulties of genetic research is the asymmetrical relationship between data collection techniques and data analysis techniques. The goal of this research was to test a novel application of non-negative matrix factorization, which would allow researchers to more easily identify co-mutations. Those co-mutations then can then be further verified by frequency analysis. This pruning process allows researchers to identify more fruitful research opportunities, saving time, energy, and funding. Past research has utilized non-negative matrix factorization to extract factors which meaningfully express underlying data features. This study extends the depth of non-negative matrix factorization knowledge in various ways. First, …

End-To-End Hierarchical Reinforcement Learning With Integrated Subgoal Discovery, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan, Chai Quek

Research Collection School Of Computing and Information Systems

Hierarchical reinforcement learning (HRL) is a promising approach to perform long-horizon goal-reaching tasks by decomposing the goals into subgoals. In a holistic HRL paradigm, an agent must autonomously discover such subgoals and also learn a hierarchy of policies that uses them to reach the goals. Recently introduced end-to-end HRL methods accomplish this by using the higher-level policy in the hierarchy to directly search the useful subgoals in a continuous subgoal space. However, learning such a policy may be challenging when the subgoal space is large. We propose integrated discovery of salient subgoals (LIDOSS), an end-to-end HRL method with an integrated …

Pickup And Multi-Delivery Problem With Time Windows, Pham Tuan Anh, Aldy Gunawan, Vincent F. Yu, Tuan C. Chau

Research Collection School Of Computing and Information Systems

This paper addresses a new variant of Pickup and Delivery Problem with Time Windows (PDPTW) for enhancing customer satisfaction. In particular, a huge number of requests is served in the system, where each request includes a pickup node and several delivery nodes instead of a pair of pickup and delivery nodes. It is named Pickup and Multi-Delivery Problem with Time Windows (PMDPTW). A mixed-integer programming model is formulated with the objective of minimizing total travel costs. Computational experiments are conducted to test the correctness of the model with a newly generated benchmark based on the PDPTW benchmark instances. Results show …

Beer: Fast O(1/T) Rate For Decentralized Nonconvex Optimization With Communication Compression, Haoyu Zhao, Boyue Li, Zhize Li, Peter Richtarik, Yuejie Chi

Research Collection School Of Computing and Information Systems

Communication efficiency has been widely recognized as the bottleneck for large-scale decentralized machine learning applications in multi-agent or federated environments. To tackle the communication bottleneck, there have been many efforts to design communication-compressed algorithms for decentralized nonconvex optimization, where the clients are only allowed to communicate a small amount of quantized information (aka bits) with their neighbors over a predefined graph topology. Despite significant efforts, the state-of-the-art algorithm in the nonconvex setting still suffers from a slower rate of convergence $O((G/T)^{2/3})$ compared with their uncompressed counterpart, where $G$ measures the data heterogeneity across different clients, and $T$ is the number …

Assessment Design For Digital Education: An Analytics-Based Authentic Assessment Approach, Lim Ming Soon Tristan, Gottipati Swapna, Michelle L. F. Cheong, Christopher Pang, Jun Wei Ng

Research Collection School Of Computing and Information Systems

This study looks to identify an assessment design construct that overcomes known issues in authentic assessment design practices in digital education. These include lack of "freedom-of-choice", lack of focus on multimodal nature of the digital process, and shortage of effective feedbacks. This study proposes an authentic assessment, that combines gamification (G) with heutagogy (H) and multimodality (M) of assessments, building upon learning analytics (A), known as GHMA. Proposed assessment design is a skills-oriented game-based assessment approach. Learners can determine their own goals and create individualized multimodal artefacts; receive cognitive challenge through cognitively complex tasks structured in gamified non-linear learning paths; …

An Efficient Annealing-Assisted Differential Evolution For Multi-Parameter Adaptive Latent Factor Analysis, Qing Li, Guansong Pang, Mingsheng Shang

Research Collection School Of Computing and Information Systems

A high-dimensional and incomplete (HDI) matrix is a typical representation of big data. However, advanced HDI data analysis models tend to have many extra parameters. Manual tuning of these parameters, generally adopting the empirical knowledge, unavoidably leads to additional overhead. Although variable adaptive mechanisms have been proposed, they cannot balance the exploration and exploitation with early convergence. Moreover, learning such multi-parameters brings high computational time, thereby suffering gross accuracy especially when solving a bilinear problem like conducting the commonly used latent factor analysis (LFA) on an HDI matrix. Herein, an efficient annealing-assisted differential evolution for multi-parameter adaptive latent factor analysis …

Mining Competitively-Priced Bundle Configurations, Ezekiel Ong Young, Hady W. Lauw

Research Collection School Of Computing and Information Systems

We examine the bundle configuration problem in the presence of competition. Given a competitor's bundle configuration and pricing, we determine what to bundle together, and at what prices, to maximize the target firm's revenue. We highlight the difficulty in pricing bundles and propose a scalable alternative and an efficient search heuristic to refine the approximate prices. Furthermore, we extend the heuristics proposed by previous work to accommodate the presence of a competitor. We analyze the effectiveness of our proposed models through experimentation on real-life ratings-based preference data.

Singlish Checker: A Tool For Understanding And Analysing An English Creole Language, Lee-Hsun Hsieh, Nam Chew Chua, Agus Trisnajaya Kwee, Pei-Chi Lo, Yang-Yin Lee, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

As English is a widely used language in many countries of different cultures, variants of English also known as English creoles have also been created. Singlish is one such English creole used by people in Singapore. Nevertheless, unlike English, Singlish is not taught in schools nor encouraged to be used in formal communications. Hence, it remains to be a low resource language with a lack of up-to-date Singlish word dictionary and computational tools to analyse the language. In this paper, we therefore propose Singlish Checker, a tool that is able to help detecting Singlish text, Singlish words and phrases. To …

Identifying Properties Of Blockchain Making It Relevant To Iot Applications, Ashwaq Alaklabi

Theses and Dissertations

Blockchain is a distributed ledger system that enables machine-to-machine interactions combined with the Internet of Things (IoT). It employs a series of transactions stored in a database, confirmed by numerous sources, and written into a shared ledger distributed across all nodes. The marriage of IoT with blockchain has several potential benefits, including the ability for a smart device to act autonomously without the need for a centralized authority. It can also monitor how devices communicate with one another. A blockchain network can track and sell almost anything of value, lowering risk and costs for everyone involved. Numerous applications from diverse …

Design Of Environment Aware Planning Heuristics For Complex Navigation Objectives, Carter D. Bailey

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

A heuristic is the simplified approximations that helps guide a planner in deducing the best way to move forward. Heuristics are valued in many modern AI algorithms and decision-making architectures due to their ability to drastically reduce computation time. Particularly in robotics, path planning heuristics are widely leveraged to aid in navigation and exploration. As the robotic platform explores and navigates, information about the world can and should be used to augment and update the heuristic to guide solutions. Complex heuristics that can account for environmental factors, robot capabilities, and desired actions provide optimal results with little wasted exploration, but …

Npgreat: Assembly Of The Human Subtelomere Regions With The Use Of Ultralong Nanopore Reads And Linked Reads, Eleni Adam, Desh Ranjan, Harold Riethman

Computer Science Faculty Publications

Background: Human subtelomeric DNA regulates the length and stability of adjacent telomeres that are critical for cellular function, and contains many gene/pseudogene families. Large evolutionarily recent segmental duplications and associated structural variation in human subtelomeres has made complete sequencing and assembly of these regions difficult to impossible for many loci, complicating or precluding a wide range of genetic analyses to investigate their function.

Results: We present a hybrid assembly method, NanoPore Guided REgional Assembly Tool (NPGREAT), which combines Linked-Read data with mapped ultralong nanopore reads spanning subtelomeric segmental duplications to potentially overcome these difficulties. Linked-Read sets of DNA sequences identified …

Enhanced Heart Rate Prediction Model Using Damped Least-Squares Algorithm, Angela An, Mohammad Al-Fawa’Reh, James Jin Kang

Research outputs 2022 to 2026

Monitoring a patient’s vital signs is considered one of the most challenging problems in telehealth systems, especially when patients reside in remote locations. Companies now use IoT devices such as wearable devices to participate in telehealth systems. However, the steady adoption of wearables can result in a significant increase in the volume of data being collected and transmitted. As these devices run on limited battery power, they can run out of power quickly due to the high processing requirements of the device for data collection and transmission. Given the importance of medical data, it is imperative that all transmitted data …

Secure Decentralized Blockchain Based Web Application For Medical Records, Sri Harshini Popuri, Liang Zhao

Symposium of Student Scholars

The online storage and sharing of electronic health records has undergone a paradigm shift in recent years. The introduction of a centralized cloud computing concept to streamline records transfer between patients and healthcare providers has been an easy task. As a result, the availability of electronically stored health records with minimal operational costs is made possible, but the primary concern is related to the privacy and security of records. How can we securely exchange medical documents online while maintaining strong security standards? This research suggests a framework that fuses online federated learning with blockchain technology. In particular, we develop a …

A Pipeline To Generate Deep Learning Surrogates Of Genome-Scale Metabolic Models, Achilles Rasquinha

School of Computing: Dissertations, Theses, and Student Research

Genome-Scale Metabolic Models (GEMMs) are powerful reconstructions of biological systems that help metabolic engineers understand and predict growth conditions subjected to various environmental factors around the cellular metabolism of an organism in observation, purely in silico. Applications of metabolic engineering range from perturbation analysis and drug-target discovery to predicting growth rates of biotechnologically important metabolites and reaction objectives within dierent single-cell and multi-cellular organism types. GEMMs use mathematical frameworks for quantitative estimations of flux distributions within metabolic networks. The reasons behind why an organism activates, stuns, or fluctuates between alternative pathways for growth and survival, however, remain relatively unknown. GEMMs …

Classroom Audio Classification Using Deep Learning Frameworks, Afsana Rahman Mou

Theses and Dissertations

For both teachers and students studying science, technology, engineering, and mathematics (STEM), active learning is more likely to be productive because learners engage in a variety of classroom activities. As instructors are trying different pedagogies in classroom, it is also important to check the effectiveness of those methods. The aim of our work is to identify the classroom activities with more accuracy which will help to measure the student involvement in the class. Using automatic audio classification, we can help to improve active learning strategies in the classroom, and it will be cost effective too. Various deep learning techniques, such …

Room-Temperature Polariton Quantum Fluids In Halide Perovskites, Kai Peng, Renjie Tao, Louis Haeberlé, Quanwei Li, Dafei Jin, Graham R. Fleming, Stéphane Kéna-Cohen, Xiang Zhang, Wei Bao

School of Computing: Faculty Publications

Quantum fluids exhibit quantum mechanical effects at the macroscopic level, which contrast strongly with classical fluids. Gain-dissipative solid-state exciton-polaritons systems are promising emulation platforms for complex quantum fluid studies at elevated temperatures. Recently, halide perovskite polariton systems have emerged as materials with distinctive advantages over other room-temperature systems for future studies of topological physics, non-Abelian gauge fields, and spin-orbit interactions. However, the demonstration of nonlinear quantum hydrodynamics, such as superfluidity and Čerenkov flow, which is a consequence of the renormalized elementary excitation spectrum, remains elusive in halide perovskites. Here, using homogenous halide perovskites single crystals, we report, in both one- …

Active Attestation Of Embedded Systems, Mark M. Stephenson, Patrick A. Reber, Patrick J. Sweeney, Scott R. Graham

AFIT Patents

An active attestation apparatus verifies at runtime the integrity of untrusted machine code of an embedded system residing in a memory device while it is being run/used with while slowing the processing time less than other methods. The apparatus uses an integrated circuit chip containing a microcontroller and a reprogrammable logic device, such as a field programmable gate array (FPGA), to implement software attestation at runtime and in less time than is typically possible with comparable attestation approaches, while not requiring any halt of the processor in the microcontroller. The reprogrammable logic device includes functionality to load an encrypted version …

Portal: Portal Widget For Remote Target Acquisition And Control In Immersive Virtual Environments, Donguyn Han, Donghoon Kim, Isaac Cho

Computer Science Student Research

This paper introduces PORTAL (POrtal widget for Remote Target Acquisition and controL) that allows the user to interact with out-of-reach objects in a virtual environment. We describe the PORTAL interaction technique for placing a portal widget and interacting with target objects through the portal. We conduct two formal user studies to evaluate PORTAL for selection and manipulation functionalities. The results show PORTAL supports participants to interact with remote objects successfully and precisely. Following that, we discuss its potential and limitations, and future works.

An Investigation Into Whitening Loss For Self-Supervised Learning, Xi Weng, Lei Huang, Lei Zhao, Rao Muhammad Anwer, Salman Khan, Fahad Shahbaz Khan

Computer Vision Faculty Publications

A desirable objective in self-supervised learning (SSL) is to avoid feature collapse. Whitening loss guarantees collapse avoidance by minimizing the distance between embeddings of positive pairs under the conditioning that the embeddings from different views are whitened. In this paper, we propose a framework with an informative indicator to analyze whitening loss, which provides a clue to demystify several interesting phenomena as well as a pivoting point connecting to other SSL methods. We reveal that batch whitening (BW) based methods do not impose whitening constraints on the embedding, but they only require the embedding to be full-rank. This full-rank constraint …

A Multiple Input Multiple Output Framework For The Automatic Optical Fractionator-Based Cell Counting In Z-Stacks Using Deep Learning, Palak Dave

USF Tampa Graduate Theses and Dissertations

Quantifying cells in a defined region of biological tissue is critical for many clinical and preclinical studies, especially in pathology, toxicology, cancer, and behavior. Unbiased stereology is the state-of-art method for quantification of the total number and other morphometric parameters of stained objects in a defined region of biological tissue. As part of a program to develop accurate, precise, and more efficient automatic approaches for quantifying morphometric changes in biological tissue, our group has shown that both deep learning-based and hand-crafted algorithms can estimate the total number of histologically stained cells at their maximal profile of focus in extended depth …

A Wind Turbine Fault Diagnosis Method Based On Siamese Deep Neural Network, Jiarui Liu, Guotian Yang, Xiaowei Wang

Journal of System Simulation

Abstract: In order to effectively extract the fault features of time series data in supervisory control and data acquisition (SCADA), considering the advantages of one-dimensional convolutional neural network (1-D CNN) for extracting local time series features and the advantages of long-term memory (LSTM) which can extract long-term dependent features, a method for fault diagnosis of wind turbines based on 1-D CNN-LSTM is proposed. To solve the problem of the scarcity of fault samples of wind turbines based on the siamese network architecture, a wind fault diagnosis method based on siamese 1-D CNN-LSTM is proposed. The proposed siamese 1-D CNN-LSTM …

Transmission Line Insulator Recognition Based On Artificial Images Data Expansion, Yaru Wang, Kai Yang, Yongjie Zhai, Congbin Guo, Wenqing Zhao, Jie Su

Journal of System Simulation

Abstract: Deep learning method has developed rapidly in the field of computer vision, but relies on a large quantities of training data. In the task of transmission line insulator automatic detection, problems such as insufficient number of aerial insulator images and poor diversity affect the accuracy of insulator recognition. An artificial insulator images data expansion method is proposed. Artificial insulator images are created by modeling software, and a compensation network is constructed. The artificial images are compensated and optimized by compensation network, and the aerial insulator image data set is expanded by the compensated artificial insulator images. The insulator recognition …

Contribution Rate Calculation Method To System-Of-Systems Based On Interval-Valued Intuitionistic Fuzzy Number Anp, Zejian Ding, Songtao Sun, Zhiwen He, Fei Liu

Journal of System Simulation

Abstract: Contribution rate to system-of-systems (CRSoS) is mainly used to measure the contribution of an equipment to system of systems (SoS) in system construction. In order to solve some problems in the calculation of CRSoS, a multi-level equipment indicator architecture of "task-ability-indicator- equipment" is proposed. At the same time, considering the characteristics of the equipment indicator architecture, ANP (analytic network process) and IVIFN (interval-valued intuitionistic fuzzy number), a IVIF-ANP calculation method is proposed to obtain more accurate CRSoS. Experiments show that this method can not only solve the problem of the calculation formula of CRSoS, but also obtain more …

Simulation And Effectiveness Evaluation System For Joint Delivery Mission Planning Of Airlift Fleets, Guochen Wang

Journal of System Simulation

Abstract: Airlift fleet plays an important role in modern war. Compared with other countries such as the USA and Russia, China's airlift fleet still has obvious shortcomings and deficiencies. To analysis and optimize the future fleet alternatives, a software tool is established with the modules of model construction and management, scenarios editing, mission planning, simulation deduction, effectiveness analysis. This tool mainly focuses on the interactive relationship between the transport aircraft and cargo, airport and so on, as well as the cooperative relationship of different types of aircraft, which can realize the functions of automatic generation of loading schemes, automatic planning …

Bilevel Distributed Optimal Dispatch Of Active Distribution Network With Multi-Microgrids, Yongjun Lin, Xin Chen, Kai Yang, Shanshan Zhou, Qingfei Bai

Journal of System Simulation

Abstract: With continuous increase of the penetration proportion of renewable energy in the distribution network, the traditional centralized dispatching is facing problems such as high pressure of power flow calculation and difficulty in recycling renewable energy, which makes it difficult to guarantee the operation quality of the system. A distributed optimal two-layer scheduling method for active distribution networks with multiple micro-grids is proposed. The upper-level aims to minimize the loss of regional distribution network, the second-order conical relaxation method and synchronous ADMM (alternating direction method of multipliers) algorithm are used to solve the scheduling instructions of the micro-grid connection lines. …